Signal estimation with binary-valued sensors

Wang, Le Yi; Yin, Gang; Li, Chanying; Zheng, Wei Xing

doi:10.1007/s11424-010-0149-4

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…Previous research on this subject considered the estimation of systems parameters when the input sequence is known [17], [18] and when the input signal is synchronously sampled, that is, when the ratio between the sampling sequence period and the signal period is a rational number [19]. Several other papers have addressed a wide class of similar problems in the context of system identification [20]- [27].…”

Section: State Of the Artmentioning

confidence: 99%

Quick Estimation of Periodic Signal Parameters From 1-Bit Measurements

Carbone

Schoukens

Moschitta

2020

IEEE Trans. Instrum. Meas.

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Section: State Of the Artmentioning

confidence: 99%

Quick Estimation of Periodic Signal Parameters From 1-Bit Measurements

Carbone

Schoukens

Moschitta

2020

IEEE Trans. Instrum. Meas.

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“…State-of-the-Art: Previous research on this subject considered the estimation of systems parameters when the input sequence is known [17], [18] and when the input signal is synchronously sampled that is when the ratio between the sampling sequence period and the signal period is a rational number [19]. Several other references have addressed a wide class of similar problems in the context of system identification [20]- [27].…”

Section: Introductionmentioning

confidence: 99%

Quick Estimation of Periodic Signal Parameters from One-bit Measurements

Carbone¹

2021

Preprint

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<div> <div> <div> <p>Estimation of periodic signals, based on quantized data, is a topic of general interest in the area of instrumentation and measurement. While several methods are available, new applications require low-power, low-complexity, and adequate estimation accuracy. In this paper, we consider the simplest possible quantization, that is binary quantization, and describe a technique to estimate the parameters of a sampled periodic signal, using a fast algorithm. By neglecting the possibility that the sampling process is triggered by some signal-derived event, sampling is assumed to be asynchronous, that is the ratio between the signal and the sampling periods is defined to be an irrational number. To preserve enough information at the quantizer output, additive Gaussian input noise is assumed as the information encoding mechanism. With respect to published techniques addressing the same problem, the proposed approach does not rely on the numerical estimation of the maximum likelihood function, but provides solutions that are very closed to this estimate. At the same time, since the main estimator is based on matrix inversion, it proves to be less time-consuming than the numerical maximization of the likelihood function, especially when solving problems with a large number of parameters. The estimation procedure is described in detail and validated using both simulation and experimental results. The estimator performance limitations are also highlighted. </p> </div> </div> </div>

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Feedback systems with communications: integrated study of signal estimation, sampling, quantization, and feedback robustness

Wang

Yin

et al. 2013

Adaptive Control & Signal

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SUMMARYFeedback systems with communication channels encounter unique challenges. Communication channels mandate signal sampling and quantization, and introduce errors, data losses, and delays. Consequently, transmitted signals must be estimated. The signal estimation introduces a dynamic system that interacts with communication channels and affects the stability and performance of the feedback system. This paper studies interactions among communications, sampling, quantization, signal estimation, and feedback, in terms of fundamental stability and performance limitations. Typical empirical‐measure‐based algorithms are used for signal estimation under quantized observations. When the sampling interval and signal estimation step size are coordinated, the ODE approach for stochastic approximations provides a suitable platform for an integrated system analysis for signal estimation, sampling and quantization, and feedback robustness. Feedback design for enhancing robustness against communication uncertainty and signal estimation dynamics is studied under the new notion of stability margin under signal averaging. Fundamental limitations on noise attenuation in such an integrated system are derived. Copyright © 2013 John Wiley & Sons, Ltd.

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Signal estimation with binary-valued sensors

Cited by 4 publications

References 12 publications

Quick Estimation of Periodic Signal Parameters From 1-Bit Measurements

Quick Estimation of Periodic Signal Parameters From 1-Bit Measurements

Quick Estimation of Periodic Signal Parameters from One-bit Measurements

Feedback systems with communications: integrated study of signal estimation, sampling, quantization, and feedback robustness

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